Methodologies & Techniques

Manova research is no mere number cruncher. Our entire focus revolves around understanding the uniqueness of your data and choosing the correct statistical technique to derive actionable analysis from its study. Some of the commonly used statistical techniques we use in the course of our analysis and interpretation include:

Summarizing data
Measures of Location, Variation and Shape
Frequency Tables
Cross Tabulation
Histogram and Box Whisker plot
Tests of Significance (Parametric)
One sample t test
T test for independent samples
Paired t test
Analysis of Variance (one way and two way)
Tests of Significance (Non Parametric)
Mann Whitney Wilcoxon’s test
Wilcoxon’s signed rank test
Kruskal Wallis Anova
Median Test
Mcnemar Test
Design of Experiments
2 * 2 cross over design
Higher Order cross over design
Balanced Incomplete Block Designs
Multivariate Techniques
Principal Component Analysis
Factor Analysis
Cluster Analysis
Canonical Correlation Analysis
Discriminant Analysis
Multivariate Analysis of Variance (MANOVA)
Statistical Modeling
Multiple Linear Regression
Binary Logistic Regression
Multinomial Logistic Regression
Forecasting techniques
ARIMA Modeling
Exponential Smoothing
Operational Research (OR) techniques
Optimization Techniques
Linear programming
Integer programming
Stochastic programming
Nonlinear programming
Transportation Problems
Markov Chains
Queuing Theory

This list is merely indicative of some of the techniques we are familiar with and do not represent the sum of our capabilities. Tell us about your project and we will revert with a proposal/technique best suited to your unique data characteristics.

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